Prashant Mahajan

Prashant Mahajan

The rapid integration of Artificial Intelligence (AI) in Higher Education (HE) is transforming personalized learning, administrative automation, and decision-making. However, this progress presents a duality, as AI adoption also introduces ethical and institutional challenges, including algorithmic bias, data privacy risks, and governance inconsistencies. To address these concerns, this study introduces the Human-Driven AI in Higher Education (HD-AIHED) Framework, ensuring compliance with UNESCO and OECD ethical standards. This conceptual research employs a qualitative meta-synthesis approach, integrating qualitative and quantitative studies to identify patterns, contradictions, and gaps in AI adoption within HE. It reinterprets existing datasets through theoretical and ethical lenses to develop governance frameworks. The study applies a participatory integrated co-system, Phased Human Intelligence, SWOC analysis, and AI ethical review boards to assess AI readiness and governance strategies for universities and HE institutions. The HD-AIHED model bridges AI research gaps, addresses global real-time challenges, and provides tailored, scalable, and ethical strategies for diverse educational contexts. By emphasizing interdisciplinary collaboration among stakeholders, this study envisions AIHED as a transparent and equitable force for innovation. The HD-AIHED framework ensures AI acts as a collaborative and ethical enabler rather than a disruptive replacement for human intelligence while advocating for responsible AI implementation in HE.

Sandipan Banerjee, Ajjen Joshi, Prashant Mahajan et al.

Building facial analysis systems that generalize to extreme variations in lighting and facial expressions is a challenging problem that can potentially be alleviated using natural-looking synthetic data. Towards that, we propose LEGAN, a novel synthesis framework that leverages perceptual quality judgments for jointly manipulating lighting and expressions in face images, without requiring paired training data. LEGAN disentangles the lighting and expression subspaces and performs transformations in the feature space before upscaling to the desired output image. The fidelity of the synthetic image is further refined by integrating a perceptual quality estimation model, trained with face images rendered using multiple synthesis methods and their crowd-sourced naturalness ratings, into the LEGAN framework as an auxiliary discriminator. Using objective metrics like FID and LPIPS, LEGAN is shown to generate higher quality face images when compared with popular GAN models like StarGAN and StarGAN-v2 for lighting and expression synthesis. We also conduct a perceptual study using images synthesized by LEGAN and other GAN models and show the correlation between our quality estimation and visual fidelity. Finally, we demonstrate the effectiveness of LEGAN as training data augmenter for expression recognition and face verification tasks.